Sample Notebook¶

Overview¶

[One or two sentences describing what you did, what data you used, and why it matters.]

Study Area: [Region or extent]
Duration: [Start month/year – End month/year]
Role: [Solo project / Team lead / Contributor]
Status: [Completed / In progress]

---

Methods & Tools¶

Data Sources

• [Dataset name and source]
• [Dataset name and source]

Processing Steps

1. Load and clean raw data
2. Compute summary statistics
3. Visualise trends with a pandas chart
4. Map spatial distribution with Folium

Tools Used

Tool	Purpose
pandas	Data loading, cleaning, aggregation
matplotlib	Static charts
folium	Interactive web maps

---

Setup¶

In [1]:

import pandas as pd
import matplotlib.pyplot as plt
import folium

import pandas as pd import matplotlib.pyplot as plt import folium

---

Data¶

Placeholder dataset — replace with your own CSV, GeoJSON, or API call.

In [ ]:

# Placeholder time-series data
# Replace with: df = pd.read_csv("your_data.csv")
data = {
    "month": ["Jan", "Feb", "Mar", "Apr", "May", "Jun",
              "Jul", "Aug", "Sep", "Oct", "Nov", "Dec"],
    "metric_a": [120, 135, 148, 162, 175, 190, 185, 178, 160, 145, 130, 118],
    "metric_b": [ 80,  88,  95, 105, 112, 120, 118, 110, 100,  92,  85,  78],
}

df = pd.DataFrame(data)
df.head()

# Placeholder time-series data # Replace with: df = pd.read_csv("your_data.csv") data = { "month": ["Jan", "Feb", "Mar", "Apr", "May", "Jun", "Jul", "Aug", "Sep", "Oct", "Nov", "Dec"], "metric_a": [120, 135, 148, 162, 175, 190, 185, 178, 160, 145, 130, 118], "metric_b": [ 80, 88, 95, 105, 112, 120, 118, 110, 100, 92, 85, 78], } df = pd.DataFrame(data) df.head()

---

Analysis¶

Summary statistics¶

In [ ]:

df[["metric_a", "metric_b"]].describe()

df[["metric_a", "metric_b"]].describe()

Trend chart¶

In [4]:

fig, ax = plt.subplots(figsize=(10, 4))

ax.plot(df["month"], df["metric_a"], marker="o", label="Metric A", color="steelblue")
ax.plot(df["month"], df["metric_b"], marker="s", label="Metric B", color="coral")

ax.set_title("[YOUR CHART TITLE]", fontsize=14, fontweight="bold")
ax.set_xlabel("Month")
ax.set_ylabel("[Unit]")
ax.legend()
ax.grid(axis="y", linestyle="--", alpha=0.5)

plt.tight_layout()
plt.show()

fig, ax = plt.subplots(figsize=(10, 4)) ax.plot(df["month"], df["metric_a"], marker="o", label="Metric A", color="steelblue") ax.plot(df["month"], df["metric_b"], marker="s", label="Metric B", color="coral") ax.set_title("[YOUR CHART TITLE]", fontsize=14, fontweight="bold") ax.set_xlabel("Month") ax.set_ylabel("[Unit]") ax.legend() ax.grid(axis="y", linestyle="--", alpha=0.5) plt.tight_layout() plt.show()

Grouped bar chart¶

---

Spatial Distribution¶

Interactive map of placeholder sample points — replace coordinates and popup text with your own data.

In [7]:

# Placeholder point locations
# Replace with your own lat/lon data, e.g. from a GeoDataFrame or CSV
sites = pd.DataFrame({
    "name":  ["Site A", "Site B", "Site C", "Site D", "Site E"],
    "lat":   [28.61,    28.65,    28.58,    28.70,    28.55],
    "lon":   [77.20,    77.23,    77.17,    77.28,    77.15],
    "value": [142,      168,      95,       210,       87],
})

# Centre the map on the mean location
m = folium.Map(
    location=[sites["lat"].mean(), sites["lon"].mean()],
    zoom_start=11,
    tiles="CartoDB positron",
)

# Add a CircleMarker for each site, scaled by value
for _, row in sites.iterrows():
    folium.CircleMarker(
        location=[row["lat"], row["lon"]],
        radius=row["value"] / 20,          # scale radius to value
        color="steelblue",
        fill=True,
        fill_opacity=0.7,
        tooltip=f"{row['name']}: {row['value']}",
        popup=folium.Popup(
            f"<b>{row['name']}</b><br>Value: {row['value']}",
            max_width=200,
        ),
    ).add_to(m)

m

# Placeholder point locations # Replace with your own lat/lon data, e.g. from a GeoDataFrame or CSV sites = pd.DataFrame({ "name": ["Site A", "Site B", "Site C", "Site D", "Site E"], "lat": [28.61, 28.65, 28.58, 28.70, 28.55], "lon": [77.20, 77.23, 77.17, 77.28, 77.15], "value": [142, 168, 95, 210, 87], }) # Centre the map on the mean location m = folium.Map( location=[sites["lat"].mean(), sites["lon"].mean()], zoom_start=11, tiles="CartoDB positron", ) # Add a CircleMarker for each site, scaled by value for _, row in sites.iterrows(): folium.CircleMarker( location=[row["lat"], row["lon"]], radius=row["value"] / 20, # scale radius to value color="steelblue", fill=True, fill_opacity=0.7, tooltip=f"{row['name']}: {row['value']}", popup=folium.Popup( f"<b>{row['name']}</b><br>Value: {row['value']}", max_width=200, ), ).add_to(m) m

Out[7]:

Make this Notebook Trusted to load map: File -> Trust Notebook

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Key Findings¶

• [Finding one — include a number or metric if possible]
• [Finding two]
• [Finding three]

---

Links¶

View Code on GitHub
View Data Source